Increasing demand for higher wireless system capacity has catalyzed several ground-breaking transmission techniques. In wireless communication systems, recent developments have been made using orthogonal frequency division multiplexing (OFDM) technologies. In OFDM schemes, the sub-carrier frequencies are spaced apart by precise frequency differences and make efficient use of the spectrum by allowing overlap. Besides, channel equalization becomes simpler than by using adaptive equalization techniques with single-carrier systems. By using multiple-input multiple-output (MIMO) schemes in OFDM systems, it is possible to increase the capacity of data. The MIMO technology has attracted the great part of recent attention. FIG. 1 is an exemplary diagram illustrating a typical MIMO system having M transmit antennas and N received antennas at k-th sub-carrier. As shown in FIG. 1, both transmit and receive sides have multiple antennas. Transmit data X is transmitted through M×N wireless channels H then received by a receiver's multiple antennas as observation data Y.
It has been shown that significant capacity gains are achievable when multi-element antennas (MEA) are used at both the transmitting and receiving sides. Spatial multiplexing techniques, for example the BLAST (Bell-labs Layered Space-Time) system, were developed to attain very high spectral efficiencies in rich scattering environments. Ideal rich-scattering environments may decorrelate channels between different pairs of transmit and receive antennas.
In practice, however, spatial correlations do exist and should be considered when designing a MIMO receiver for evaluating the corresponding system performance. Spatial correlation depends on physical parameters such as antenna spacing, antenna arrangement, and scatters' distributions. Antenna correlations reduce the number of equivalent orthogonal sub-channels, decrease spectral efficiency, making it more difficult to detect the transmitted data. A coherent MIMO receiver requires an accurate channel estimate to perform critical operations and provide satisfactory performance. Not only is reliable channel estimation mandatory in guaranteeing signal reception quality but it is also needed in designing an adequate precoder at the transmitting side to achieve maximum throughput or minimum bit error rate in feedback MIMO systems.
Various pilot-assisted MIMO channel estimators have been disclosed. For example, one disclosed method exploits the sparsity structure of MIMO channels. A channel estimation process or a pilot placement and pilot allocation process can be taken for the sparse channel estimation of MIMO inter-symbol interference (ISI) channels in MIMO-OFDM systems. The front or back end of a channel estimator may utilize the channel estimation output by the scheme. In the scheme, there is no fixed basis for precoder at the transmitter end or for codebook selector at the receiver end.
Another document disclosed estimating channel parameters in MIMO systems. Referring to FIG. 2, one embodiment performed by the channel parameter estimator includes calculating coarse channel estimates using an least square (LS) method and/or a Zero forcing method (step 202), performing a frequency domain interpolation procedure (step 204) to the subcarriers that were not excited, and reducing the mean square error (step 206). In addition to the estimation of channel parameters, the parameter estimator may further calculate estimates of the “noise variance,” which is a parameter representing the power of the extraneous unwanted noise present in the signal. An exemplary embodiment for noise variance estimation includes calculating a noise term from coarse and final channel estimates (step 212), multiplying the noise term with a signal transmission matrix for each tone (step 214), and converting the frequency domain coefficients to the time domain (step 216), and calculating noise variance estimate for each receive antenna (step 218). There is also no fixed basis for precoder at the transmitter end or for codebook selector at the receiver end. While only one frequency basis is used for the channel estimator.
Yet another document disclosed a technology for reduced rank channel estimation in a communication system. Referring to FIG. 3, the technology exploits redundant and/or a priori knowledge within a system to simplify the estimation calculation including estimating significant delays of the channel (step 310), producing full dimension channel estimates (step 312), and calculating a covariance matrix of channels (step 314). The covariance matrix is further analyzed to determine if the channel parameters may be reduced for channel estimation. If not, use the full rank of the system to model the channel (step 320), otherwise a reduced rank matrix is used for the calculation including estimating the channel subspace (step 322) and reduced rank channel parameters (step 324), and transforming channel parameters back to full dimension (step 326). There is also no fixed basis for precoder at the transmitter end or for codebook selector at the receiver end. While spatial basis is provided in real time for the channel estimation.
In conventional designs, closed-loop MIMO systems provide high capacity and robust performance when accurate channel state information (CSI) is available (often through feedback). Codebook-based solutions are used to reduce the CSI bandwidth requirement. Few estimators are specifically designed for correlated MIMO channels, and those few exploited only channel's time and frequency correlation characteristics by approximating the time- and/or frequency-domain response by an analytic model. In fact, no method or apparatus exists for MIMO system that is capable of using spatial, frequency and time correlation as well. Thus a need exists for a method and apparatus that is capable of using time/frequency/spatial correlation and may further provide accurate estimates, compact and useful CSI, and potential post processing complexity cutbacks.